Zero-shot learning: Humans can recognize an object by relating known objects, without prior visual experience. Simulating this behavior into an automated machine vision system is called Zero-shot learning (ZSL). ZSL attempts to recognize unseen objects without any visual examples of the unseen category. In recent years, numerous effective methods for ZSL have been proposed. Every ZSL strategy has to relate seen and unseen embedding through semantic embedding vector. Based on how this relation is established, we can categorize ZSL strategies into three types. The first type of methods attempt to predict the semantic vector of classes [1]}, [2]}, [3]}, [4]}. An object is classified as an unseen class based on similarity of predicted vector and semantic vectors of unseen classes. Predicting a high dimensional vector is not an efficient way to related seen-unseen classes because it cannot work consistently if the semantic vectors are noisy [5]}. This reason provokes this kind of methods to use attributes as semantic embedding as they are less noisy. The second kind of methods learn a linear [6]}, [7]}, [8]} or non-linear [9]}, [10]} compatibility function to relate the seen image feature and corresponding semantic vector. This compatibility function yields high value if visual feature and semantic vector come from the same class and vice versa. A visual feature is classified to an unseen class if it gets the best compatibility score among all possible unseen classes. Such methods work consistently across a wide variety of semantic embedding vectors. The third kind of methods describe unseen classes by mixing seen visual features and semantic embedding [11]}, [12]}, [13]}. For this mixing purpose, sometimes methods perform per class learning and later combine individual class output to decide outputs for unseen classes. While most of the ZSL approaches convert visual feature to semantic spaces, [14]}, [15]} mapped semantic vectors to the visual domain to address the hubness problem during prediction [16]}. Irrespective of method types, attributes work better as semantic embeddings compared to unsupervised word2vec, GloVe, and hierarchies because of less noise. To minimize this performance gap, researchers have investigated transductive setting [17]}, [18]}, [19]}, domain adaptation [20]}, [21]} and class-attribute association [22]}, [23]} techniques. Usually, all ZSL methods are evaluated on a restricted case of recognition problem where test data only contain unseen images. Few recent efforts performed experiments on generalized version of ZSL [18]}, [25]}, [19]}. They found that established ZSL methods perform poorly in such settings. Still, all these methods perform a recognition task in zero-shot settings. In this paper, we extend recognition problem to a more complex detection problem.
